Systems and techniques for performing a scan-free reading of a structured identifier are provided. One technique involves obtaining a first set of images of an object within a scanning volume of an environment. The object is passed through the scanning volume without an explicit presentation of the structured identifier to an optical scanner within the environment. For each image of a plurality of the first set of images, an indication of a first region of interest of the structured identifier on the object within the image is obtained, based in part on a second set of images of the object having a lower quality than the first set of images. The first region of interest of the structured identifier is extracted from a first image of the plurality of the first set of images. The structured identifier within the first region of interest within the first image is decoded.

Systems and techniques for performing a scan-free reading of a structured identifier are provided. One technique involves obtaining a first set of images of an object within a scanning volume of an environment. The object is passed through the scanning volume without an explicit presentation of the structured identifier to an optical scanner within the environment. For each image of a plurality of the first set of images, an indication of a first region of interest of the structured identifier on the object within the image is obtained, based in part on a second set of images of the object having a lower quality than the first set of images. The first region of interest of the structured identifier is extracted from a first image of the plurality of the first set of images. The structured identifier within the first region of interest within the first image is decoded.

Method, apparatus and computer program for providing a personalized study plan to a learner through cognitive and behavioral diagnosis of the learner. A learner who uses a data input device such as a smart pen and a stylus pen by using data obtained from the data input device. The method, apparatus and computer program relate to technology for obtaining input data based on information inputted by a user for at least one question with the data input device, creating test behavior data on the user from the obtained input data, analyzing cognition and behavior of the user based on at least one of metadata on the at least one question and the created test behavior data, and providing a personalized study plan to the user through an algorithm using machine learning based on the cognition and behavior analysis.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining analysis profiles, where each analysis profile: corresponds to one of a plurality of detectors, and comprises: a unique set of analysis parameters and/or a unique detection algorithm. The method further includes analyzing image data in accordance with the analysis profiles; selecting an optimum analysis result based on confidence scores associated with different analysis results; and detecting objects within the optimum analysis result. According to additional aspects, the analysis parameters may define different subregions of a digital image to be analyzed; a composite analysis result may be generated based on analysis of the different subregions by different detectors; and the optimum analysis result may be based on the composite analysis result.

An information processing apparatus includes a processor configured to evaluate a performance of each of a plural combinations of processes, each combination including a series of processes, the plural combinations being included in plural processes, the series of processes being configured to be performed on an image, the performance being evaluated on a basis of a result of performing the series of processes on a test image for each combination, and output an evaluation result for at least two combinations.

An object recognition method related to the field of artificial intelligence comprises: collecting an object to be subjected to recognition (S101); according to a target text detection model corresponding to the object to be subjected to recognition, carrying out screening and recognition on full text information corresponding to the object to be subjected to recognition, so as to obtain point-of-interest text information therefrom (S102); and carrying out recognition on the point-of-interest text information according to a preset text recognition model (S103). A target text detection model obtains point-of-interest text information by means of carrying out screening and recognition on full text information, such that the recognition of full text information in the prior art is avoided, thus saving recognition time, and improving the recognition efficiency.

A method, an apparatus, a device, a storage medium and a program product of performing a text matching are provided, which relate to a field of a computer technology, and in particular to natural language processing and deep learning technologies. The method includes: determining a word set and a plurality of semantic units from a text set, the word set is associated with a first predetermined attribute, and the text set contains a plurality of first texts indicating an object information and a plurality of second texts indicating an object demand information; generating a graph; and generating a final feature representation associated with the text set and the word set based on the graph and a graph convolution model, so as to perform the text matching.

An image processing method, an image processing device, an electronic apparatus, and a non-transitory computer-readable storage medium are provided. The image processing method includes: obtaining an original image; performing slicing processing on the original image to obtain multiple slice images of the original image; respectively processing the slice images through a first binarization model to obtain multiple slice binary images respectively corresponding to the slice images; performing stitching processing on the slice binary images to obtain a first binary image; processing the first binary image to obtain a pixel circumscribed contour image; processing the original image through a second binarization model to obtain a second binary image; synthesizing the second binary image and the first binary image according to positions of multiple circumscribed contour pixels in the pixel circumscribed contour image to obtain a synthetic image.

A method includes transmitting a request for a computer-readable routing diagram, such as a wiring diagram, a pneumatic or hydraulic line diagram, or an optical cable diagram. The method also includes receiving, in response to the request, a routing diagram including a connectable component and at least a portion of a routing set. The connectable component is associated with both a connectable component identifier and the routing set. The routing set contains one or more routable components, such as electrical circuits, pneumatic or hydraulic lines, or optical cables. Further, the method includes displaying, on a display, the routing diagram and at least the portion of the routing set highlighted. Displaying at least the portion of the routing set highlighted occurs in response to an occurrence of the connectable component being selected from the routing diagram displayed on the display or in response to the request including the connectable component identifier.

A content aware and style aware neural network based data augmentation model generates augmented data sets to train neural network based handwriting recognition models to recognize individuals' handwriting. The augmented data sets may be generated so as to be artificial, and to lack personal or confidential information. The data augmentation model may generate content reference sets of individual characters generated in different fonts, and style reference sets of pluralities of characters of a particular style, for example, an individual's handwriting.